{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Banking_Queries_Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tOHVDa9DQQR5"
      },
      "source": [
        "![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/Banking_Queries_Classification.ipynb)\n",
        "\n",
        "\n",
        "# [DistilBERT Sequence Classification - Industry](https://nlp.johnsnowlabs.com/2021/11/21/distilbert_sequence_classifier_banking77_en.html)\n",
        "\n",
        "Fine-tuned DistilBERT model by using Banking77 dataset. The dataset is composed of online banking queries annotated with their corresponding intents.\n",
        "\n",
        "BANKING77 dataset provides a very fine-grained set of intents in a banking domain. It comprises 13,083 customer service queries labeled with 77 intents. It focuses on fine-grained single-domain intent detection.\n",
        "\n",
        "It can be used to identify and sort help requests which would greatly increase the efficency of workers and also help provide automatic support . \n",
        "\n",
        "It can predict the following Entities\n",
        "\n",
        "`activate_my_card`, `age_limit`, `apple_pay_or_google_pay`, `atm_support`, `automatic_top_up`, `balance_not_updated_after_bank_transfer`, `balance_not_updated_after_cheque_or_cash_deposit`, `beneficiary_not_allowed`, `cancel_transfer`, `card_about_to_expire`, `card_acceptance`, `card_arrival`, `card_delivery_estimate`, `card_linking`, `card_not_working`, `card_payment_fee_charged`, `card_payment_not_recognised`, `card_payment_wrong_exchange_rate`, `card_swallowed`, `cash_withdrawal_charge`, `cash_withdrawal_not_recognised`, `change_pin`, `compromised_card`, `contactless_not_working`, `country_support`, `declined_card_payment`, `declined_cash_withdrawal`, `declined_transfer`, `direct_debit_payment_not_recognised`, `disposable_card_limits`, `edit_personal_details`, `exchange_charge`, `exchange_rate`, `exchange_via_app`, `extra_charge_on_statement`, `failed_transfer`, `fiat_currency_support`, `get_disposable_virtual_card`, `get_physical_card`, `getting_spare_card`, `getting_virtual_card`, `lost_or_stolen_card`, `lost_or_stolen_phone`, `order_physical_card`, `passcode_forgotten`, `pending_card_payment`, `pending_cash_withdrawal`, `pending_top_up`, `pending_transfer`, `pin_blocked`, `receiving_money`, `Refund_not_showing_up`, `request_refund`, `reverted_card_payment?`, `supported_cards_and_currencies`, `terminate_account`, `top_up_by_bank_transfer_charge`, `top_up_by_card_charge`, `top_up_by_cash_or_cheque`, `top_up_failed`, `top_up_limits`, `top_up_reverted`, `topping_up_by_card`, `transaction_charged_twice`, `transfer_fee_charged`, `transfer_into_account`, `transfer_not_received_by_recipient`, `transfer_timing`, `unable_to_verify_identity`, `verify_my_identity`, `verify_source_of_funds`, `verify_top_up`, `virtual_card_not_working`, `visa_or_mastercard`, `why_verify_identity`, `wrong_amount_of_cash_received`, `wrong_exchange_rate_for_cash_withdrawal`\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1.Setup Java 8 and NLU"
      ],
      "metadata": {
        "id": "HIxATMI7ixJx"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SF5-Z-U4jukd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49f6abad-3baa-4703-cd35-8147ad178f36"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh -O - | bash\n",
        "\n",
        "import nlu"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-05-19 22:58:41--  https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1665 (1.6K) [text/plain]\n",
            "Saving to: ‘STDOUT’\n",
            "\n",
            "\r-                     0%[                    ]       0  --.-KB/s               Installing  NLU 3.4.4rc1 with  PySpark 3.0.3 and Spark NLP 3.4.3 for Google Colab ...\n",
            "\r-                   100%[===================>]   1.63K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2022-05-19 22:58:41 (1.10 MB/s) - written to stdout [1665/1665]\n",
            "\n",
            "Get:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n",
            "Get:2 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n",
            "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:4 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
            "Hit:7 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease\n",
            "Ign:8 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n",
            "Hit:9 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n",
            "Hit:10 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n",
            "Hit:11 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n",
            "Get:12 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease [21.3 kB]\n",
            "Get:13 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [2,765 kB]\n",
            "Get:14 http://security.ubuntu.com/ubuntu bionic-security/multiverse amd64 Packages [22.8 kB]\n",
            "Get:15 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,503 kB]\n",
            "Get:16 http://security.ubuntu.com/ubuntu bionic-security/restricted amd64 Packages [932 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,277 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,199 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [29.8 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [966 kB]\n",
            "Get:22 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 Packages [44.3 kB]\n",
            "Fetched 12.0 MB in 3s (3,625 kB/s)\n",
            "Reading package lists... Done\n",
            "tar: spark-3.0.2-bin-hadoop2.7.tgz: Cannot open: No such file or directory\n",
            "tar: Error is not recoverable: exiting now\n",
            "\u001b[K     |████████████████████████████████| 209.1 MB 58 kB/s \n",
            "\u001b[K     |████████████████████████████████| 144 kB 20.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 517 kB 40.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 198 kB 44.9 MB/s \n",
            "\u001b[?25h  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##2. Load the mdoel and make Sample Predictions "
      ],
      "metadata": {
        "id": "XiVdjjfzij2R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline = nlu.load('en.classify.distilbert_sequence.banking77')\n",
        "pipeline.predict(\"I am still waiting on my card?\",output_level='document')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "zzWPoMYZsVj2",
        "outputId": "21cdcecc-48be-4142-a86d-15b406e5c91d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "distilbert_sequence_classifier_banking77 download started this may take some time.\n",
            "Approximate size to download 235.1 MB\n",
            "[OK!]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  classified_sequence classified_sequence_confidence  \\\n",
              "0        card_arrival                       0.989413   \n",
              "\n",
              "                         document  \n",
              "0  I am still waiting on my card?  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-60c5924c-4464-4388-9af3-41f11fa78c34\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>classified_sequence</th>\n",
              "      <th>classified_sequence_confidence</th>\n",
              "      <th>document</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>card_arrival</td>\n",
              "      <td>0.989413</td>\n",
              "      <td>I am still waiting on my card?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60c5924c-4464-4388-9af3-41f11fa78c34')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-60c5924c-4464-4388-9af3-41f11fa78c34 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-60c5924c-4464-4388-9af3-41f11fa78c34');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##3.Define Sample Sentences"
      ],
      "metadata": {
        "id": "_XcMyXVXsdYW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample_sentences = [\n",
        "\"How many countries do you support?\",\n",
        "\"I am still waiting on my card?\t\",\n",
        "\"Can the card be mailed and used in Europe?\"\n",
        "]"
      ],
      "metadata": {
        "id": "c_ty0gP6sglE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##4.Predict on Sample Sentences"
      ],
      "metadata": {
        "id": "qpD0_HqksiLw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline.predict(sample_sentences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "zynQvfNmWI50",
        "outputId": "af84e651-d7f2-4f13-dbcb-2ac6ac615880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sentence_detector_dl download started this may take some time.\n",
            "Approximate size to download 354.6 KB\n",
            "[OK!]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  classified_sequence classified_sequence_confidence  \\\n",
              "0     country_support                       0.989771   \n",
              "1        card_arrival                       0.989413   \n",
              "2     country_support                       0.993269   \n",
              "\n",
              "                                     sentence  \n",
              "0          How many countries do you support?  \n",
              "1              I am still waiting on my card?  \n",
              "2  Can the card be mailed and used in Europe?  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-255914e0-4a84-4e69-a00b-1a67fede709c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>classified_sequence</th>\n",
              "      <th>classified_sequence_confidence</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>country_support</td>\n",
              "      <td>0.989771</td>\n",
              "      <td>How many countries do you support?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>card_arrival</td>\n",
              "      <td>0.989413</td>\n",
              "      <td>I am still waiting on my card?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>country_support</td>\n",
              "      <td>0.993269</td>\n",
              "      <td>Can the card be mailed and used in Europe?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-255914e0-4a84-4e69-a00b-1a67fede709c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-255914e0-4a84-4e69-a00b-1a67fede709c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-255914e0-4a84-4e69-a00b-1a67fede709c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##5.Take a look at the parmaters of the pipeline"
      ],
      "metadata": {
        "id": "cEf6CWsxtDWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline.print_info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TgLjxNltPCp",
        "outputId": "680a1da0-8514-42e8-b19e-131a89d7239d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",
            ">>> component_list['distil_bert_for_sequence_classification'] has settable params:\n",
            "component_list['distil_bert_for_sequence_classification'].setActivation('softmax')                         | Info: Whether to calcuate logits via Softmax or Sigmoid. Default is Softmax | Currently set to : softmax\n",
            "component_list['distil_bert_for_sequence_classification'].setBatchSize(8)                                  | Info: Size of every batch | Currently set to : 8\n",
            "component_list['distil_bert_for_sequence_classification'].setCoalesceSentences(False)                      | Info: Instead of 1 class per sentence (if inputCols is '''sentence''') output 1 class per document by averaging probabilities in all sentences. | Currently set to : False\n",
            "component_list['distil_bert_for_sequence_classification'].setMaxSentenceLength(512)                        | Info: Max sentence length to process | Currently set to : 512\n",
            "component_list['distil_bert_for_sequence_classification'].setCaseSensitive(True)                           | Info: whether to ignore case in tokens for embeddings matching | Currently set to : True\n",
            ">>> component_list['document_assembler'] has settable params:\n",
            "component_list['document_assembler'].setCleanupMode('shrink')                                              | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",
            ">>> component_list['tokenizer'] has settable params:\n",
            "component_list['tokenizer'].setTargetPattern('\\S+')                                                        | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n",
            "component_list['tokenizer'].setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"])  | Info: character list used to separate from token boundaries | Currently set to : ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]\n",
            "component_list['tokenizer'].setCaseSensitiveExceptions(True)                                               | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n",
            "component_list['tokenizer'].setMinLength(0)                                                                | Info: Set the minimum allowed legth for each token | Currently set to : 0\n",
            "component_list['tokenizer'].setMaxLength(99999)                                                            | Info: Set the maximum allowed legth for each token | Currently set to : 99999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking Good! Let's test this model on a labelled dataset to see how it performs "
      ],
      "metadata": {
        "id": "1hF8dmLIkbSz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##6.Download Data\n",
        "\n",
        "we are going to test the model on [this](https://huggingface.co/datasets/banking77) dataset \n",
        "\n",
        "Dataset composed of online banking queries annotated with their corresponding intents.\n",
        "\n",
        "BANKING77 dataset provides a very fine-grained set of intents in a banking domain. It comprises 13,083 customer service queries labeled with 77 intents. It focuses on fine-grained single-domain intent detection.\n"
      ],
      "metadata": {
        "id": "uRbusvKOkVac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://ckl-it.de/wp-content/uploads/2022/04/Bank_Data.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvD0rZTBrnrL",
        "outputId": "1d1c68c3-e647-4c49-90ed-860de0453e59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-05-19 22:48:40--  http://ckl-it.de/wp-content/uploads/2022/04/Bank_Data.csv\n",
            "Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n",
            "Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1065932 (1.0M) [text/csv]\n",
            "Saving to: ‘Bank_Data.csv’\n",
            "\n",
            "Bank_Data.csv       100%[===================>]   1.02M  1.08MB/s    in 0.9s    \n",
            "\n",
            "2022-05-19 22:48:41 (1.08 MB/s) - ‘Bank_Data.csv’ saved [1065932/1065932]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas  as pd \n",
        "df = pd.read_csv(\"Bank_Data.csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "n-6bOT4krpr8",
        "outputId": "9ce81e48-ba0f-4551-c8d8-2612b478db80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text            label\n",
              "0                         I am still waiting on my card?     card_arrival\n",
              "1      What can I do if my card still hasn't arrived ...     card_arrival\n",
              "2      I have been waiting over a week. Is the card s...     card_arrival\n",
              "3      Can I track my card while it is in the process...     card_arrival\n",
              "4      How do I know if I will get my card, or if it ...     card_arrival\n",
              "...                                                  ...              ...\n",
              "13078      If i'm not in the UK, can I still get a card?  country_support\n",
              "13079                 How many countries do you support?  country_support\n",
              "13080              What countries do you do business in?  country_support\n",
              "13081             What are the countries you operate in.  country_support\n",
              "13082         Can the card be mailed and used in Europe?  country_support\n",
              "\n",
              "[13083 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-21861c2b-5e84-4926-9a4e-a2b13f80c882\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I am still waiting on my card?</td>\n",
              "      <td>card_arrival</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>What can I do if my card still hasn't arrived ...</td>\n",
              "      <td>card_arrival</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I have been waiting over a week. Is the card s...</td>\n",
              "      <td>card_arrival</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Can I track my card while it is in the process...</td>\n",
              "      <td>card_arrival</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>How do I know if I will get my card, or if it ...</td>\n",
              "      <td>card_arrival</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13078</th>\n",
              "      <td>If i'm not in the UK, can I still get a card?</td>\n",
              "      <td>country_support</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13079</th>\n",
              "      <td>How many countries do you support?</td>\n",
              "      <td>country_support</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13080</th>\n",
              "      <td>What countries do you do business in?</td>\n",
              "      <td>country_support</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13081</th>\n",
              "      <td>What are the countries you operate in.</td>\n",
              "      <td>country_support</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13082</th>\n",
              "      <td>Can the card be mailed and used in Europe?</td>\n",
              "      <td>country_support</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13083 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21861c2b-5e84-4926-9a4e-a2b13f80c882')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-21861c2b-5e84-4926-9a4e-a2b13f80c882 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-21861c2b-5e84-4926-9a4e-a2b13f80c882');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take  a Peek at the distribution of the labels "
      ],
      "metadata": {
        "id": "mUl3_RmhuSHW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.label.value_counts().plot.barh(title='Distribution of Labels', figsize=(20,14))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "7b9cKEyMtbWL",
        "outputId": "47cc7b4a-c143-4061-eaa1-1ea26162cdcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fba2ecb9f90>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1008 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##7.Make Predictions with the model"
      ],
      "metadata": {
        "id": "KHxOy5o9uyTG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predctions = pipeline.predict(df,output_level = 'document')"
      ],
      "metadata": {
        "id": "QpmPv2q5uVyd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predctions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "j4JpnxjzMOsV",
        "outputId": "cc5ea183-2e18-4c5b-dfc7-8a59d1b00335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      classified_sequence classified_sequence_confidence  \\\n",
              "0            card_arrival                       0.989413   \n",
              "1            card_arrival                       0.989708   \n",
              "2            card_arrival                       0.984588   \n",
              "3            card_arrival                       0.980207   \n",
              "4            card_arrival                       0.947395   \n",
              "...                   ...                            ...   \n",
              "13078     country_support                        0.99499   \n",
              "13079     country_support                       0.989771   \n",
              "13080     country_support                       0.994252   \n",
              "13081     country_support                       0.994301   \n",
              "13082     country_support                       0.993269   \n",
              "\n",
              "                                                document            label  \\\n",
              "0                         I am still waiting on my card?     card_arrival   \n",
              "1      What can I do if my card still hasn't arrived ...     card_arrival   \n",
              "2      I have been waiting over a week. Is the card s...     card_arrival   \n",
              "3      Can I track my card while it is in the process...     card_arrival   \n",
              "4      How do I know if I will get my card, or if it ...     card_arrival   \n",
              "...                                                  ...              ...   \n",
              "13078      If i'm not in the UK, can I still get a card?  country_support   \n",
              "13079                 How many countries do you support?  country_support   \n",
              "13080              What countries do you do business in?  country_support   \n",
              "13081             What are the countries you operate in.  country_support   \n",
              "13082         Can the card be mailed and used in Europe?  country_support   \n",
              "\n",
              "                                                    text  \n",
              "0                         I am still waiting on my card?  \n",
              "1      What can I do if my card still hasn't arrived ...  \n",
              "2      I have been waiting over a week. Is the card s...  \n",
              "3      Can I track my card while it is in the process...  \n",
              "4      How do I know if I will get my card, or if it ...  \n",
              "...                                                  ...  \n",
              "13078      If i'm not in the UK, can I still get a card?  \n",
              "13079                 How many countries do you support?  \n",
              "13080              What countries do you do business in?  \n",
              "13081             What are the countries you operate in.  \n",
              "13082         Can the card be mailed and used in Europe?  \n",
              "\n",
              "[13083 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-988b3ecb-1ac0-4e0a-aea1-8c102fbb39a8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>classified_sequence</th>\n",
              "      <th>classified_sequence_confidence</th>\n",
              "      <th>document</th>\n",
              "      <th>label</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>card_arrival</td>\n",
              "      <td>0.989413</td>\n",
              "      <td>I am still waiting on my card?</td>\n",
              "      <td>card_arrival</td>\n",
              "      <td>I am still waiting on my card?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>card_arrival</td>\n",
              "      <td>0.989708</td>\n",
              "      <td>What can I do if my card still hasn't arrived ...</td>\n",
              "      <td>card_arrival</td>\n",
              "      <td>What can I do if my card still hasn't arrived ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>card_arrival</td>\n",
              "      <td>0.984588</td>\n",
              "      <td>I have been waiting over a week. Is the card s...</td>\n",
              "      <td>card_arrival</td>\n",
              "      <td>I have been waiting over a week. Is the card s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>card_arrival</td>\n",
              "      <td>0.980207</td>\n",
              "      <td>Can I track my card while it is in the process...</td>\n",
              "      <td>card_arrival</td>\n",
              "      <td>Can I track my card while it is in the process...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>card_arrival</td>\n",
              "      <td>0.947395</td>\n",
              "      <td>How do I know if I will get my card, or if it ...</td>\n",
              "      <td>card_arrival</td>\n",
              "      <td>How do I know if I will get my card, or if it ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13078</th>\n",
              "      <td>country_support</td>\n",
              "      <td>0.99499</td>\n",
              "      <td>If i'm not in the UK, can I still get a card?</td>\n",
              "      <td>country_support</td>\n",
              "      <td>If i'm not in the UK, can I still get a card?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13079</th>\n",
              "      <td>country_support</td>\n",
              "      <td>0.989771</td>\n",
              "      <td>How many countries do you support?</td>\n",
              "      <td>country_support</td>\n",
              "      <td>How many countries do you support?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13080</th>\n",
              "      <td>country_support</td>\n",
              "      <td>0.994252</td>\n",
              "      <td>What countries do you do business in?</td>\n",
              "      <td>country_support</td>\n",
              "      <td>What countries do you do business in?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13081</th>\n",
              "      <td>country_support</td>\n",
              "      <td>0.994301</td>\n",
              "      <td>What are the countries you operate in.</td>\n",
              "      <td>country_support</td>\n",
              "      <td>What are the countries you operate in.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13082</th>\n",
              "      <td>country_support</td>\n",
              "      <td>0.993269</td>\n",
              "      <td>Can the card be mailed and used in Europe?</td>\n",
              "      <td>country_support</td>\n",
              "      <td>Can the card be mailed and used in Europe?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13083 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-988b3ecb-1ac0-4e0a-aea1-8c102fbb39a8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-988b3ecb-1ac0-4e0a-aea1-8c102fbb39a8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-988b3ecb-1ac0-4e0a-aea1-8c102fbb39a8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##8.Evaluate Predictions "
      ],
      "metadata": {
        "id": "y03ZPigmGPYL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(predctions['label'], predctions['classified_sequence']) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfJtQLGDu8nN",
        "outputId": "0a90f9c9-682f-4566-a40f-b7bf972495a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                  precision    recall  f1-score   support\n",
            "\n",
            "                           Refund_not_showing_up       1.00      1.00      1.00       202\n",
            "                                activate_my_card       1.00      0.99      1.00       199\n",
            "                                       age_limit       1.00      1.00      1.00       150\n",
            "                         apple_pay_or_google_pay       0.99      1.00      1.00       166\n",
            "                                     atm_support       0.95      1.00      0.97       127\n",
            "                                automatic_top_up       1.00      0.98      0.99       167\n",
            "         balance_not_updated_after_bank_transfer       0.95      0.96      0.96       211\n",
            "balance_not_updated_after_cheque_or_cash_deposit       1.00      0.99      0.99       221\n",
            "                         beneficiary_not_allowed       0.97      0.98      0.97       196\n",
            "                                 cancel_transfer       0.98      0.99      0.99       197\n",
            "                            card_about_to_expire       0.99      1.00      0.99       169\n",
            "                                 card_acceptance       0.91      0.97      0.94        99\n",
            "                                    card_arrival       0.99      0.94      0.97       193\n",
            "                          card_delivery_estimate       0.94      0.99      0.96       152\n",
            "                                    card_linking       1.00      0.99      0.99       179\n",
            "                                card_not_working       0.96      0.93      0.95       152\n",
            "                        card_payment_fee_charged       0.98      0.99      0.99       227\n",
            "                     card_payment_not_recognised       0.98      0.96      0.97       208\n",
            "                card_payment_wrong_exchange_rate       0.98      0.98      0.98       207\n",
            "                                  card_swallowed       1.00      0.97      0.98       101\n",
            "                          cash_withdrawal_charge       0.98      1.00      0.99       217\n",
            "                  cash_withdrawal_not_recognised       0.97      0.99      0.98       200\n",
            "                                      change_pin       0.99      0.98      0.98       162\n",
            "                                compromised_card       0.98      0.94      0.96       126\n",
            "                         contactless_not_working       1.00      0.96      0.98        75\n",
            "                                 country_support       0.98      1.00      0.99       169\n",
            "                           declined_card_payment       0.95      0.99      0.97       193\n",
            "                        declined_cash_withdrawal       0.95      0.97      0.96       213\n",
            "                               declined_transfer       0.98      0.92      0.95       173\n",
            "             direct_debit_payment_not_recognised       0.95      0.98      0.96       222\n",
            "                          disposable_card_limits       0.99      0.98      0.98       161\n",
            "                           edit_personal_details       1.00      1.00      1.00       161\n",
            "                                 exchange_charge       0.99      0.98      0.98       161\n",
            "                                   exchange_rate       0.97      0.99      0.98       152\n",
            "                                exchange_via_app       0.96      0.99      0.97       158\n",
            "                       extra_charge_on_statement       1.00      0.99      0.99       206\n",
            "                                 failed_transfer       0.97      0.94      0.96       177\n",
            "                           fiat_currency_support       0.99      0.96      0.97       166\n",
            "                     get_disposable_virtual_card       0.96      0.98      0.97       137\n",
            "                               get_physical_card       0.99      0.99      0.99       146\n",
            "                              getting_spare_card       0.99      0.99      0.99       169\n",
            "                            getting_virtual_card       0.98      0.99      0.99       138\n",
            "                             lost_or_stolen_card       0.94      0.98      0.96       122\n",
            "                            lost_or_stolen_phone       1.00      0.99      0.99       161\n",
            "                             order_physical_card       0.98      0.98      0.98       160\n",
            "                              passcode_forgotten       0.99      0.97      0.98       145\n",
            "                            pending_card_payment       0.98      0.98      0.98       199\n",
            "                         pending_cash_withdrawal       1.00      0.98      0.99       183\n",
            "                                  pending_top_up       0.97      0.97      0.97       189\n",
            "                                pending_transfer       0.98      0.94      0.96       188\n",
            "                                     pin_blocked       0.98      0.97      0.98       155\n",
            "                                 receiving_money       0.99      0.98      0.98       135\n",
            "                                  request_refund       1.00      0.99      1.00       209\n",
            "                          reverted_card_payment?       0.95      0.98      0.97       201\n",
            "                  supported_cards_and_currencies       0.95      1.00      0.98       169\n",
            "                               terminate_account       0.98      1.00      0.99       148\n",
            "                  top_up_by_bank_transfer_charge       0.99      0.97      0.98       151\n",
            "                           top_up_by_card_charge       0.99      0.97      0.98       154\n",
            "                        top_up_by_cash_or_cheque       0.98      0.99      0.98       154\n",
            "                                   top_up_failed       0.97      0.93      0.95       185\n",
            "                                   top_up_limits       0.96      0.99      0.97       137\n",
            "                                 top_up_reverted       0.95      0.94      0.94       186\n",
            "                              topping_up_by_card       0.94      0.94      0.94       143\n",
            "                       transaction_charged_twice       0.99      1.00      0.99       215\n",
            "                            transfer_fee_charged       0.99      1.00      0.99       212\n",
            "                           transfer_into_account       0.97      0.95      0.96       153\n",
            "              transfer_not_received_by_recipient       0.97      0.98      0.97       211\n",
            "                                 transfer_timing       0.94      0.98      0.96       168\n",
            "                       unable_to_verify_identity       0.97      0.93      0.95       142\n",
            "                              verify_my_identity       0.90      0.97      0.93       144\n",
            "                          verify_source_of_funds       1.00      1.00      1.00       153\n",
            "                                   verify_top_up       0.99      0.99      0.99       166\n",
            "                        virtual_card_not_working       1.00      0.90      0.95        81\n",
            "                              visa_or_mastercard       1.00      0.98      0.99       175\n",
            "                             why_verify_identity       0.94      0.93      0.93       161\n",
            "                   wrong_amount_of_cash_received       1.00      0.98      0.99       220\n",
            "         wrong_exchange_rate_for_cash_withdrawal       0.99      0.98      0.99       203\n",
            "\n",
            "                                        accuracy                           0.98     13083\n",
            "                                       macro avg       0.98      0.98      0.98     13083\n",
            "                                    weighted avg       0.98      0.98      0.98     13083\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXu21c0iQRSC"
      },
      "source": [
        "# There are many more models you can put to use in 1 line of code!\n",
        "## Checkout [the Modelshub](https://nlp.johnsnowlabs.com/models) and the [NLU Namespace](https://nlu.johnsnowlabs.com/docs/en/spellbook) for more models\n",
        "\n",
        "### NLU Webinars and Video Tutorials\n",
        "- [NLU & Streamlit Tutorial](https://vimeo.com/579508034#)\n",
        "- [Crash course of the 50 + Medical Domains and the 200+ Healtchare models in NLU](https://www.youtube.com/watch?v=gGDsZXt1SF8)\n",
        "- [Multi Lingual NLU Webinar - Tutorial on Chinese News dataset](https://www.youtube.com/watch?v=ftAOqJuxnV4)\n",
        "- [John Snow Labs NLU: Become a Data Science Superhero with One Line of Python code](https://events.johnsnowlabs.com/john-snow-labs-nlu-become-a-data-science-superhero-with-one-line-of-python-code?hsCtaTracking=c659363c-2188-4c86-945f-5cfb7b42fcfc%7C8b2b188b-92a3-48ba-ad7e-073b384425b0)\n",
        "- [Python Web Def Conf - Python's NLU library: 1,000+ Models, 200+ Languages, State of the Art Accuracy, 1 Line of Code](https://2021.pythonwebconf.com/presentations/john-snow-labs-nlu-the-simplicity-of-python-the-power-of-spark-nlp)\n",
        "- [NYC/DC NLP Meetup with NLU](https://youtu.be/hJR9m3NYnwk?t=2155)\n",
        "\n",
        "### More ressources \n",
        "- [Join our Slack](https://join.slack.com/t/spark-nlp/shared_invite/zt-lutct9gm-kuUazcyFKhuGY3_0AMkxqA)\n",
        "- [NLU Website](https://nlu.johnsnowlabs.com/)\n",
        "- [NLU Github](https://github.com/JohnSnowLabs/nlu)\n",
        "- [Many more NLU example tutorials](https://github.com/JohnSnowLabs/nlu/tree/master/examples)\n",
        "- [Overview of every powerful nlu 1-liner](https://nlu.johnsnowlabs.com/docs/en/examples)\n",
        "- [Checkout the Modelshub for an overview of all models](https://nlp.johnsnowlabs.com/models) \n",
        "- [Checkout the NLU Namespace where you can find every model as a tabel](https://nlu.johnsnowlabs.com/docs/en/spellbook)\n",
        "- [Intro to NLU article](https://medium.com/spark-nlp/1-line-of-code-350-nlp-models-with-john-snow-labs-nlu-in-python-2f1c55bba619)\n",
        "- [Indepth and easy Sentence Similarity Tutorial, with StackOverflow Questions using BERTology embeddings](https://medium.com/spark-nlp/easy-sentence-similarity-with-bert-sentence-embeddings-using-john-snow-labs-nlu-ea078deb6ebf)\n",
        "- [1 line of Python code for BERT, ALBERT, ELMO, ELECTRA, XLNET, GLOVE, Part of Speech with NLU and t-SNE](https://medium.com/spark-nlp/1-line-of-code-for-bert-albert-elmo-electra-xlnet-glove-part-of-speech-with-nlu-and-t-sne-9ebcd5379cd)"
      ]
    }
  ]
}